IG-CEE: An Embedded Information Gain approach to Genetic Algorithms
Alexandre Henrick da Silva Alves, Raphael de Lima Mendes, Matheus de Souza Gomes, Pedro Luiz Lima Bertarini, Laurence Rodrigues do Amaral · 2021
The classification task is among the most used in Data Mining and it is widely researched nowadays. Some works have already been developed using Genetic Algorithms (GAs) for classification tasks, building simple and small IF-THEN classification rules with good classification results. These methods produce understandable outputs (IF-THEN rules), unlike some of the traditional classifiers, e.g. SVM and Artificial Neural Networks, known as black-box type. In this paper, we proposed a new method called Information Gain-based Computational Evolutionary Environment (IG-CEE). The proposed method IG-CEE extends the GA proposed by Amaral and Hruschka named Computational Evolutionary Environment (CEE). The IG-CEE uses the Information Gain approach to select a better subset of attributes to compose the IF-THEN rules, improving classification accuracy and convergence rates. The IG-CEE was compared with CEE and four (4) traditional classifiers: J48, IBK, Naive Bayes, and SVM. To compare all methods, we built three (3) synthetic datasets, generated by GeNIe software. To realize a fair comparison, all methods were executed, with distinct random seeds, 100 times. For each method, the final result was calculated using a confidence interval with 95% of confidence. The proposed method IG-CEE showed better classification rates in several comparisons.